The Impact of the Aging Population on Coronary Heart Disease in the United States

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1 CLINICAL RESEARCH STUDY The Impact of the Aging Population on Coronary Heart Disease in the United States Michelle C. Odden, PhD, a Pamela G. Coxson, PhD, a Andrew Moran, MD, MPH, b,c James M. Lightwood, PhD, d Lee Goldman, MD, MPH, b Kirsten Bibbins-Domingo, PhD, MD a,e a Department of Medicine, University of California, San Francisco; b College of Physicians and Surgeons, Columbia University, New York, NY; c Division of General Medicine, Columbia University Medical Center, New York, NY; d Department of Pharmacy, University of California, San Francisco; e Department of Epidemiology and Biostatistics, University of California, San Francisco. ABSTRACT BACKGROUND: The demographic shift toward an older population in the United States will result in a higher burden of coronary heart disease, but the increase has not been quantified in detail. We sought to estimate the impact of the aging US population on coronary heart disease. METHODS: We used the Coronary Heart Disease Policy Model, a Markov model of the US population between 35 and 84 years of age, and US Census projections to model the age structure of the population between 2010 and RESULTS: Assuming no substantive changes in risks factors or treatments, incident coronary heart disease is projected to increase by approximately 26%, from 981,000 in 2010 to 1,234,000 in 2040, and prevalent coronary heart disease by 47%, from 11.7 million to 17.3 million. Mortality will be affected strongly by the aging population; annual coronary heart disease deaths are projected to increase by 56% over the next 30 years, from 392,000 to 610,000. Coronary heart disease-related health care costs are projected to rise by 41% from $126.2 billion in 2010 to $177.5 billion in 2040 in the United States. It may be possible to offset the increase in disease burden through achievement of Healthy People 2010/2020 objectives or interventions that substantially reduce obesity, blood pressure, or cholesterol levels in the population. CONCLUSIONS: Without considerable changes in risk factors or treatments, the aging of the US population will result in a sizeable increase in coronary heart disease incidence, prevalence, mortality, and costs. Health care stakeholders need to plan for the future age-related health care demands of coronary heart disease Elsevier Inc. All rights reserved. The American Journal of Medicine (2011) 124, KEYWORDS: Aging; Coronary heart disease; Forecasting; Markov chains Funding: Supported in part by the Swanson Family Fund, Tempe, Ariz, and a grant-in-aid from the American Heart Association Western States Affiliate, Burlingame, Calif. (09GRNT ). Dr. Odden is supported by a Ruth L. Kirschstein National Research Service Award (T32HP19025). Dr. Moran is supported by an NIH/NHLBI Mentored Career Development Award (K08HL089675) and an Empire Clinical Research Program award from New York State. Conflict of Interest: None. Authorship: All authors were involved in the design of the study, interpretation of data, and the writing of the manuscript. All authors have approved the final version. Requests for reprints should be addressed to Michelle C. Odden, PhD, Department of Medicine, University of California, San Francisco, Box 1211, San Francisco, CA address: michelle.odden@ucsf.edu The US population is aging. The combination of the elevated birth rate during the baby boom and increasing life expectancy will result in a doubling of the population aged 65 years and older from year 2010 to 2040, 1 with the number of persons aged 65 and older increasing from 40 million in 2010 to 81 million in ,2 Coronary heart disease is strongly associated with age and is the leading cause of death in the US. 3 Because coronary heart disease disproportionately affects the elderly, this demographic shift will result in a considerable increase in burden of disease in the US population. The first of the baby boomers will enter the Medicare population in 2011, so up-to-date projections of coronary heart disease burden are of special importance for public health planning. Quantitative estimates of the magnitude and distribution of disease are /$ -see front matter 2011 Elsevier Inc. All rights reserved. doi: /j.amjmed

2 828 The American Journal of Medicine, Vol 124, No 9, September 2011 essential for appropriate preparedness strategies at both the provider and system levels, and in order to optimally target preventive efforts. We aimed to provide a detailed projection of the impact of the aging US population on coronary heart disease burden, based on a computer model that is representative of the US population. The Coronary Heart Disease Policy Model is a simulation model that can account for the relationship of risk factors and changing demographics on the incidence, prevalence, and cost of coronary heart disease. 4 Since its inception, the model has been used to explain the decline in coronary heart disease mortality in the United States 5 ; to estimate the gains in life expectancy achievable by various risk factor interventions 6 ; to assess the cost-effectiveness of interventions to reduce cholesterol, 7,8 blood pressure, 9,10 and smoking 11 ; and to analyze the public health impact of improvements in medical care. 12,13 Most recently, the model has assessed CLINICAL SIGNIFICANCE the impact of current adolescent obesity on future rates of adult coronary heart disease, 14 public policies to reduce passive tobacco exposure on coronary heart disease rates, 15 clinical guidelines for the use of statins, 16 and the impact of dietary salt reductions. 17 In 1987, the model s projections were published through In this analysis, we provide updated estimates of the impact of the aging US population on the incidence, prevalence, mortality, and cost of coronary heart disease from 2010 to In addition, we estimate the extent to which achievement of national goals for risk factor improvement could offset the age-related increase in coronary heart disease burden. METHODS The Coronary Heart Disease Policy Model The Coronary Heart Disease Policy Model is a validated state-transition (Markov) model of the incidence, prevalence, mortality, and cost of coronary heart disease in US residents 35 to 84 years of age. 4 The model has 3 components. First, the demographic-epidemiologic submodel estimates the incidence of coronary heart disease (cardiac arrest, myocardial infarction, angina, or coronary heart disease death) and death from other causes, based on age, sex, systolic blood pressure, smoking, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol, diabetes, and body mass index (BMI). Second, the bridge submodel characterizes the initial coronary heart disease event and related events in the subsequent 30 days. Third, the disease history submodel predicts the number of Absolute coronary heart disease incidence is projected to increase by 26%, prevalence by 47%, mortality by 56%, and costs by 41% because of the aging of the US population between 2010 to Achievement of Healthy People 2010/ 2020 major objectives for risk factor control could offset the majority of the projected increase. A 10% reduction in body mass index in adults also could substantially attenuate the impact of the aging population. subsequent coronary heart disease events, revascularization procedures, and deaths among subjects with coronary heart disease, stratified according to age, sex, and history of coronary heart disease events. Modifiable components of the model include: population distributions, risk factor levels, risk factor coefficients, event rates, case fatality rates, costs, and quality of life adjustments. 4 A more detailed description of the model is given in the Appendix (available online). Data Sources In the present study, we used US Census data and 2008 projections to estimate the demographic shift in the age structure of the population. 2 Other data sources for the model include the National Center for Health Statistics mortality data, 18 the Framingham Heart Study for the association of risk factors with coronary heart disease, 19, 20 and Olmsted County data for the incidence of myocardial infarction and cardiac arrest. 21 The prevalence of coronary heart disease was estimated from the National Health Interview Survey, 22 and the risk factor distributions were estimated from the National Health and Nutrition Examination Survey. 23 Data from the National Hospital Discharge Survey were used to calculate rates of myocardial infarction, hospitalization due to cardiac arrest, revascularization procedures, and associated case fatalities. 24 We estimated coronary heart disease deaths, prehospital deaths caused by cardiac arrest, and non-coronary heart disease deaths based on data from the US Vital Statistics. 18 Total health care costs and 30-day survival rates were based on data from Medicare. 25 We estimated coronary heart disease costs based on California data, 26 deflated by using cost-to-charge ratios, 27 and the ratio of the US national average costs to the California average. 28 We then inflated to 2010 dollars by using the Bureau of Labor Statistics Consumer Price Index for Medical Care Costs. 29 Simulations We ran the main simulation from years 2010 to 2040 to reflect the changing age demographics of the US population over these years. Age- and sex-specific risk factor distributions, event rates, case fatality rates, and costs per event were held constant across the simulation period in order to isolate the effect of the aging population. We estimated the absolute and relative annual excess (defined as increase above 2010 estimates) in coronary heart disease incidence (stable or unstable angina, myocardial infarction, cardiac arrest, and death), prevalence, mortality, and costs across 30

3 Odden et al Heart Disease in the Aging US Population 829 Table 1 Projected Coronary Heart Disease Incidence, Prevalence, Mortality, and Cost in US Adults 35 to 84 Years of Age Year Population Incidence Total 981,000 1,128,000 1,212,000 1,234, y 658, , , , y 323, , , ,000 Prevalence Total 11,744,000 14,382,000 16,699,000 17,322, y 5,662,000 6,949,000 7,107,000 7,634, y 6,082,000 7,433,000 9,592,000 9,688,000 Mortality Total 392, , , , y 106, , , , y 286, , , ,000 Cost (in Billions) Total $126.2 $149.5 $170.7 $ y $69.7 $79.5 $80.5 $ y $56.5 $70.0 $90.2 $91.1 years. We present the annual number of events by 10-year age categories. We next explored how changes in risk factors and treatments might alter the projected increase in coronary heart disease incidence. We modeled the impact of achieving adherence to the major 2010/2020 Healthy People guidelines for risk factors in 2010, including a 10% reduction in the prevalence of hypertension, a 40% improvement in the control of hypertension, a 10% reduction in the prevalence of hypercholesterolemia, a 10% reduction in the mean level of cholesterol, a 10% reduction in the prevalence of obesity, a reduction of the prevalence of smoking to 12%, a 10% reduction in environmental exposure to smoke, and a 10% reduction in the rate of developing incident diabetes. 30 For the cholesterol interventions, we modified LDL cholesterol levels instead of total cholesterol in the model. We explored the impact of lowering LDL cholesterol and blood pressure to levels used in prevention guidelines. 31 We also modeled the impact a linear 10% increase in BMI over 30 years, which is consistent with recently published trends, 32 as well as a 10% decrease in BMI over the simulation period. Finally, we modeled the impact of a hypothetical intervention that would reduce the in-hospital myocardial infarction case fatality rate by 25% to 50%, based on improvements in the survival of patients hospitalized for myocardial infarction in the past 30 years. 33,34 RESULTS In 2010, an estimated 156 million adults 35 to 84 years of age will be living in the US. This population is expected to increase by 28% to 200 million in The 65- to 84- year-old population is expected to increase by 89% percent over this period because of the aging of baby boomers and their longer life expectancy. The increase in coronary heart disease will reflect this population growth: incidence, prevalence, mortality, and cost are all expected to increase monotonically over the next 30 years. The number of incident coronary heart disease cases is projected to increase in parallel with the overall population growth by approximately 26% from 981,000 in 2010 to 1,234,000 in 2040 (Table 1). The number of incident cases is projected to remain fairly constant in persons under 65 years of age because of the relatively smaller population growth in this subgroup. The largest increase in incident coronary heart disease events will be expected in persons 65 years of age and older, and the number of cases in 75- to 84-year-olds will double in the next 30 years. Without a change in the risk factor distributions, the incidence rate is projected to remain fairly constant, at 680 per 100,000 person-years in 2010 as compared with 710 per 100,000 person-years in The number of prevalent coronary heart disease cases also is expected to increase as a result of the change in population demographics: the number of persons with prevalent coronary heart disease is expected to grow by 47% from 11.7 million to 17.3 million in 2040 (Table 1). As with incidence, the greatest growth in prevalence is expected to be in adults over 65 years of age (Figure 1). Coronary heart disease mortality will be impacted most strongly by the shift in age demographics, and coronary heart disease deaths are expected to increase by over 50% over the next 30 years (Figure 2). The number of deaths in adults 65 to 84 years of age is projected to increase by two thirds because of the large increase in this segment of the population. The greater increase in coronary heart disease deaths compared with incidence or prevalence is because death disproportionately occurs at older ages; the increase in the number of older adults will affect mortality rates more strongly than it will affect incidence or prevalence rates. US coronary heart disease-related health care costs in adults 35 to 84 years of age are projected to rise by 41% over the next 30 years, from $126.2 billion in 2010 to $177.5 billion in 2040, based on year 2010 dollars. The annual cost of coronary heart disease per working-aged

4 830 The American Journal of Medicine, Vol 124, No 9, September 2011 Figure 1 Absolute coronary heart disease prevalence by decade of age, from 2010 to adult (20-64 years of age) is projected to rise by 24%, from $680 in 2010 to $840 in The proportion of costs associated with acute coronary heart disease care is projected to increase slightly over the next 30 years compared with costs because of chronic disease follow-up, from 48% in 2010 to 50% in Effects of Potential Changes in Risk Factors or Treatment Immediate achievement of Healthy People 2010/2020 objectives for blood pressure, lipids, obesity, smoking, and diabetes could offset the increase in disease attributable to the aging population by over 70% in 2040 (Figure 3). In addition, a trend of a reduction in BMI by 10% over the next 30 years could offset the projected increase in incidence by approximately 80%. In contrast, a trend of increase in the Figure 2 Absolute number of coronary heart disease deaths by decade of age, from 2010 to average BMI of US adults by 10% over the next 30 years would add to the burden of coronary heart disease, and could more than double the increase in incident cases by 2040 (Figure 3). If all Americans 35 to 84 years of age had a systolic blood pressure no higher than 140 mm Hg, the projected incidence of coronary heart disease would be 1,130,000 new cases in 2040, or a 15% increase over Similarly, if all Americans had an LDL cholesterol level of 130 mg/dl, the projected incidence would be 1,151,000 in 2040, or a 17% increase over A 25% to 50% reduction in the in-hospital case fatality for both first and recurrent myocardial infarction in 2040 as compared with 2010 was projected to reduce mortality in 2040 by only 4% to 8%. DISCUSSION Unprecedented growth in the 65 years of age and older population in the United States is expected over the coming decades. This growth will result in a substantial increase in coronary heart disease incidence, prevalence, mortality, and cost. A quantitative estimate of the magnitude of this increase is essential information for stakeholders in the US health care system, including patients, providers, training organizations, hospitals, health plans, governments, and industry. 35,36 Despite concern over the impact of the aging baby boomers on health care, few detailed quantitative estimates of the effect of the aging population on coronary heart disease burden have been published. Our study provides estimates that will inform researchers, providers, and policy makers as they plan to meet future age-related health care demands. Our findings expand upon previous research that has shown strong growth in coronary heart disease prevalence and mortality over the next several decades 37 ; Foot et al 37 estimated a 128.5% increase in coronary heart disease mortality and a 93% increase in coronary heart disease preva-

5 Odden et al Heart Disease in the Aging US Population 831 Figure 3 The projected impact of changes in coronary heart disease risk factors on coronary heart disease incidence, from 2010 to Adherence to Healthy People 2010/2020 objectives include immediate improvements in blood pressure, low-density lipoprotein cholesterol, obesity, smoking, and diabetes. Body mass index increase and decrease refers to a 10% trend from 2010 to lence from 2000 to Our projections are slightly more conservative, and we provide detailed epidemiologic estimates that account for the distribution of age, gender, and risk factors in the US population. As noted in previous studies, the aging population will impact the need for more focused efforts on primary, secondary, and tertiary prevention, because optimal treatment can lead to longer and higher quality of life in coronary heart disease patients. However, these preventive efforts also will increase the need for providers and services. The increased burden of disease will require more resources in hospitals, physicians, medical groups, training institutions, and public health departments. 37 Physicians and nonphysician clinicians are already in short supply. For example, the Council on Graduate Medical Education (COGME) has determined that the demand for physicians is expected to grow more than the supply over the next 10 years. 35,38 Although some of this burden will require more specialty providers to conduct procedure-based treatments, the majority of the burden is likely to fall on primary care physicians and geriatricians who manage the chronic care of patients living with coronary heart disease. Risk factors and medical advances to prevent and treat coronary heart disease will certainly change in the next 30 years, and these changes are difficult to predict. Primary prevention gains would need to be substantial to offset the impact of the aging population. The achievement of optimistic, but feasible, improvements in risk factor control, consistent with Healthy People 2010/2020 objectives, could offset more than 70% of the increase in coronary heart disease incidence related to aging of the population. However, achievement of these goals would require these improvements across several domains, including a reversal of the trend of weight gain and increasing diabetes in the United States. If the trend of increasing BMI continues at the same rate observed over the past few decades, the growing burden of coronary heart disease would be even worse. A previous study based on the model showed a substantial impact of current adolescent obesity on the future burden of coronary heart disease; Bibbins-Domingo et al 14 estimated an excess of at least 100,000 cases of coronary heart disease in Conversely, improvements in BMI could offset the increase in coronary heart disease; a 10% reduction in BMI over the next 30 years would offset approximately 80% of the impact of the aging population because of its beneficial effects on blood pressure, cholesterol, and diabetes. These simulations suggest that public health interventions to lower BMI through improved diet and physical activity should be a high priority. By comparison, a 25% to 50% reduction in in-hospital myocardial infarction case fatality would have only a modest effect on coronary heart disease, largely because the current survival rate is approximately 90% for a hospitalized first myocardial infarction. 24 Taken together, these analyses suggest that substantial improvements from risk factor profiles would be necessary to offset the projected increase in coronary heart disease. This increased burden of disease will likely tax the already struggling US health care system. Medical expenditures have increased at a high rate for many years, and the future impact of population aging on health care costs is frequently cited as a source of concern in the literature and

6 832 The American Journal of Medicine, Vol 124, No 9, September 2011 media. 36,39,40 Our cost projections related to coronary heart disease events are similar to those reported by Martini et al, 36 who estimated a 44% increase in health care costs because of heart and vascular conditions from 2000 to Notably, heart and vascular conditions account for more medical care costs than any other category of disease. 36 Population growth amongst working adults could help to offset these costs slightly, through increased tax revenue and other financial contributions. Coronary heart disease costs per working adult aged 20 to 64 years are projected to increase by 24% over the next 30 years. Economic growth will only augment the increase in health care costs; previous studies have demonstrated that economic expansion determines the ceiling for health care expenditures, and demand for services will continue to grow as patients desire and expect longer and healthier lives. 35 The model is tested and updated regularly to reflect changes in risk factor distributions, population estimates, risk factor associations, event rates, case fatality rates, and costs, although these statistics are only estimates of what may occur in the future. Projections from any forecasting model should be viewed with caution, because there will always be unpredicted changes that can impact results. The previous projections from the Coronary Heart Disease Policy Model published in 1987 underestimated the prevalence of future coronary heart disease but overestimated incidence and mortality, 4 although the majority of the variation can be explained by a change in disease definition and by reductions in risk factors and advances in treatment (see Appendix online). Our projections have limitations that should be considered when interpreting the findings. These estimates do not account for changes in the racial/ethnic structure of the population; although this could impact our estimates, the relationship between age and risk of coronary heart disease events does not likely differ greatly across racial/ethnic groups. The timing and sustainability of the risk factor changes that were modeled were optimistic and could vary substantially in practice. Furthermore, there will likely be unexpected changes in risk factor profiles and health care technology that could affect our results. Improved therapies to treat coronary heart disease may decrease secondary events and mortality, but also increase the cost and prevalence of coronary heart disease. Finally, the model does not include incident events for adults over 85 years of age, who represent a growing proportion of the population of adults with coronary heart disease. In conclusion, the demographic shift towards an increased number of older adults in the US will have a substantial impact on coronary heart disease burden over the next 30 years. Stakeholders in the US health care system should prepare for this growth by ensuring that there are adequate resources to care for the high number of adults expected to be living with coronary heart disease, because optimal treatment can lead to longer and higher quality of life in coronary heart disease patients. Future improvements in primary and secondary prevention may attenuate this burden of disease, although substantial changes would be necessary to offset the effect of the aging population. References 1. Centers for Disease Control and Prevention. Trends in aging United States and worldwide. MMWR Morb Mortal Wkly Rep. 2003;52: US Census Bureau. U.S. interim projections by age, sex, race, and Hispanic origin vol 2009, Heron M, Hoyert DL, Murphy SL, et al. Deaths: final data for Natl Vital Stat Rep. 2009;57: Weinstein MC, Coxson PG, Williams LW, et al. Forecasting coronary heart disease incidence, mortality, and cost: the Coronary Heart Disease Policy Model. Am J Public Health. 1987;77: Goldman L. Cost-effectiveness perspectives in coronary heart disease. Am Heart J. 1990;119(3 part 2): Tsevat J, Weinstein MC, Williams LW, et al. Expected gains in life expectancy from various coronary heart disease risk factor modifications. Circulation 1991;83: Goldman L, Weinstein MC, Williams LW. Relative impact of targeted versus populationwide cholesterol interventions on the incidence of coronary heart disease. Projections of the Coronary Heart Disease Policy Model. Circulation 1989;80: Prosser LA, Stinnett AA, Goldman PA, et al. Cost-effectiveness of cholesterol-lowering therapies according to selected patient characteristics. Ann Intern Med. 2000;132: Edelson JT, Weinstein MC, Tosteson AN, et al. Long-term costeffectiveness of various initial monotherapies for mild to moderate hypertension. JAMA. 1990;263: Phillips KA, Shlipak MG, Coxson P, et al. Health and economic benefits of increased beta-blocker use following myocardial infarction. JAMA. 2000;284: Tosteson AN, Weinstein MC, Williams LW, et al. Long-term impact of smoking cessation on the incidence of coronary heart disease. Am J Public Health. 1990;80: Goldman L, Phillips KA, Coxson P, et al. The effect of risk factor reductions between 1981 and 1990 on coronary heart disease incidence, prevalence, mortality and cost. J Am Coll Cardiol. 2001;38: Tice JA, Ross E, Coxson PG, et al. Cost-effectiveness of vitamin therapy to lower plasma homocysteine levels for the prevention of coronary heart disease: effect of grain fortification and beyond. JAMA. 2001;286: Bibbins-Domingo K, Coxson P, Pletcher MJ, et al. Adolescent overweight and future adult coronary heart disease. N Engl J Med. 2007; 357: Lightwood JM, Coxson PG, Bibbins-Domingo K, et al. Coronary heart disease attributable to passive smoking: CHD Policy Model. Am J Prev Med. 2009;36: Pletcher MJ, Lazar L, Bibbins-Domingo K, et al. Comparing impact and cost-effectiveness of primary prevention strategies for lipid-lowering. Ann Intern Med. 2009;150: Bibbins-Domingo K, Chertow GM, Coxson PG, et al. Projected effect of dietary salt reductions on future cardiovascular disease. N Engl J Med. 2010;362: Deaths for 358 selected causes, by 5-year age groups, race and sex: United States, Atlanta, GA: National Center for Health Statistics; Framingham Heart Study CD-ROM. Washington, DC: Department of Health and Human Services; Wilson PW, D Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97: Roger VL, Jacobsen SJ, Weston SA, et al. Trends in the incidence and survival of patients with hospitalized myocardial infarction, Olmsted County, Minnesota, 1979 to Ann Intern Med. 2002;136:

7 Odden et al Heart Disease in the Aging US Population National Health Interview Survey datasets. Washtington, DC: National Center for Health Statistics; National Health and Nutrition Examination Survey. Atlanta, GA: Centers for Disease Control and Prevention; National Hospital Discharge Survey, Atlanta, GA: Centers for Disease Control and Prevention, National Center for Health Statistics; Centers for Medicare and Medicaid Services. Personal health care spending by type of spending, age group, and source of payment distribution, calendar year Woodland, MD: Centers for Medicare and Medicaid Services; State of California, Office of Statewide Health Planning and Development. Patient discharge data file documentation, January 1-December 31, 2000, public version A-24. Sacramento, CA: Office of Statewide Health Planning and Development; Office of Statewide Health Planning and Development. Hospital financial data for cost to charge ratio, CA inpatient discharge data hospital annual finanacial data, pivot profiles, Sacramento, CA: California Office of Statewide Health Planning and Development; US Census Bureau. Statistical abstract of the United States. Average cost to community hospitals per patient, by state (Table 204). Washington, DC: Goverment Printing Office; Bureau of Labor Statistics. Consumer price index. Washington, DC: Bureau of Labor Statistics; US Department of Health and Human Services. Healthy People Washington, DC; Available at /default.aspx. Accessed May 6, Pearson TA, Blair SN, Daniels SR, et al. AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke: 2002 Update: Consensus Panel Guide to Comprehensive Risk Reduction for Adult Patients Without Coronary or Other Atherosclerotic Vascular Diseases. American Heart Association Science Advisory and Coordinating Committee. Circulation. 2002;106: Ogden CL, Fryar CD, Carroll MD, Flegal KM. Mean body weight, height, and body mass index, United States Adv Data. 2004;27: Floyd KC, Yarzebski J, Spencer FA, et al. A 30-year perspective ( ) into the changing landscape of patients hospitalized with initial acute myocardial infarction: Worcester Heart Attack Study. Circ Cardiovasc Qual Outcomes. 2009;2: McGovern PG, Jacobs Jr DR, Shahar E, et al. Trends in acute coronary heart disease mortality, morbidity, and medical care from 1985 through 1997: the Minnesota heart survey. Circulation. 2001;104: Cooper RA. Weighing the evidence for expanding physician supply. Ann Intern Med. 2004;141: Martini EM, Garrett N, Lindquist T, Isham GJ. The boomers are coming: a total cost of care model of the impact of population aging on health care costs in the United States by Major Practice Category. Health Serv Res. 2007;42(1 part 1): Foot DK, Lewis RP, Pearson TA, Beller GA. Demographics and cardiology, J Am Coll Cardiol. 2000;35: Council on Graduate Medical Education. Reassessing physician workforce policy guidelines for the U.S Washington, DC: U.S. Department of Health and Human Services; Halvorson GC. Credibility and creativity: a conversation with Kaiser Permanente s George C. Halvorson. Health Aff (Millwood). 2004;23: Rice DP, Fineman N. Economic implications of increased longevity in the United States. Annu Rev Public Health. 2004;25:

8 833.e1 The American Journal of Medicine, Vol 124, No 9, September 2011 APPENDIX: The Coronary Heart Disease Policy Model is a computersimulation, state-transition (Markov) model of coronary heart disease incidence, prevalence, mortality, and costs in the US population over 35 years of age. In the version of the model used for this analysis, the US population age 35 to 85 years, without a history of coronary heart disease, was apportioned into 4860 risk cells defined by 6 modifiable risk factors: systolic blood pressure (SBP) ( 130, 130 to 139.9, and 140 mm Hg), low-density lipoprotein (LDL) cholesterol ( 2.6, 2.6 to 3.3,and 3.4 mol/l; 100, 100 to 129.9, and 130 mg/dl), high-density lipoprotein (HDL; 1.0, 1.0 to 1.5, and 1.6 mol/l; 40, 40 to 59.9, and 60 mg/dl), smoking status (active smoker, nonsmoker with exposure to environmental tobacco smoke, or nonsmoker without environmental exposure), body mass index (BMI; 25, 25 to 29.9, or 30 kg/m 2 ), and diabetes mellitus (yes or no), as well as by sex and 10-year age range. The population with prevalent coronary heart disease was apportioned into 1300 cells according to their age, sex, history of myocardial infarction (MI), arrest, angina, and/or revascularization. Coronary heart disease incidence and noncoronary heart disease deaths in the population without previous coronary heart disease were determined by logistic risk functions based on Framingham longitudinal data. 1 Transitions in the disease history component of the model were based on age-range specific event and case fatality rates estimated from national data, and literature-based relative risks of events among disease history subgroups (eg, previous MI vs none). Noncoronary heart disease death rates in the population with coronary heart disease reflect the relative risk of noncoronary heart disease death for this population in the Framingham data. In the absence of evidence of a trend, all of these rates were assumed to remain constant. Absolute numbers of events vary with temporal changes in the population, the age range distribution of the population, and in response to user-defined interventions. All population distributions, risk factor levels, coefficients, event rates, case fatality rates, costs, and quality of life adjustments can be modified for forecasting simulations. We ran the model on Fortran 95 (Lahey Computer Systems, Incline Village, Nev). Population Estimates Population estimates for the adult US population 35 years of age and older in 2000 were obtained from the US Census 2000 data by age and sex. We used projections (2010 to 2040) of the 35-year-old population, based on the 2008 national projections. 2 Coronary Heart Disease Prevalence We estimated the background prevalence of coronary heart disease in 2000 from the National Health Interview Survey. 3 We estimated the background prevalence of previous revascularization procedures from revascularizations before 2000 and estimated survival after revascularization from the National Hospital Discharge Survey (NHDS) 4 and other studies. 5,6 Coronary Heart Disease Deaths We obtained data on coronary heart disease deaths from the 2000 Vital Statistics Mortality Data. 7 We estimated coronary heart disease deaths on the basis of International Classification of Diseases, 10th revision, (ICD10) codes I20 to I25, I46, and 2/3 of I49, I50, and I51. 8,9 We considered other deaths to be noncoronary heart disease deaths. Arrest (Sudden Death) with Resuscitation The number who survive arrest to hospital discharge was estimated from NHDS 4 for 1990 to Because the numbers are very small in any given year, we averaged the national estimates over the 10-year period. Estimates of prehospital arrest fatalities were based on Vital Statistics mortality data for selected causes by place of death. 10 For ICD10 codes I20 to I25, all emergency room deaths and those dead on arrival were assumed to be deaths from arrests. All nursing home deaths were considered to be chronic coronary heart disease deaths. In-residence and other place deaths were estimated to have had resuscitation attempted based on reported resuscitation rates for witnessed 11 or unwitnessed 12 cardiac arrest. Proportion of Arrests with no History of Coronary Heart Disease The coronary heart disease history of arrest patients is harder to ascertain than for MI because there is no national registry, because the numbers are smaller, and because fewer studies are available. We estimated the age range specific proportions of arrest with and without a history of coronary heart disease by a least squares fit to data from several sources. 13,14 Myocardial Infarction We estimated MI incidence as the average annual number of discharges coded as 410 in the NHDS 2000 data set. We eliminated records of MI in which hospital stay was fewer than 3 days and no acute revascularization was done in the same hospitalization as probable rule out MI cases. We reduced remaining counts by the double count fraction reported by Westfall and McGloin 15 and applied an additional 3% deduction for miscoding, as reported by Petersen et al. 16 Myocardial Infarction Case Fatality Rates We obtained mean number of MI deaths per adjusted total MI from the NHDS, 1996 to 2000, for the older age ranges (65 to 84 years) and used the National Registry of Myocardial Infarction 17 for in-hospital case fatality rates for the younger age ranges (35 to 44 years). Studies of young patients with MI estimate an in-hospital mortality rate of 1% to 6%, compared with a rate of 8% to 22% for older patients. 18

9 Odden et al Heart Disease in the Aging US Population 833.e2 We estimated in-hospital and 30-day case fatality rates from hospital discharge records from the State of California Office of Statewide Health Planning and Development for the year The in-hospital case fatality rate was based on unique person records (duplicate entries were eliminated by matching social security numbers). We omitted a small number of records that did not have social security numbers. The overall rate ratio of 30-day case fatality rate to inhospital case fatality rate was (12.07/9.36). We used this ratio to adjust national in-hospital case fatality rates to 30-day mortality rates. On the basis of a study by Rieves et al, 20 we incorporated a mortality odds ratio of 1.6 for patients with previous MI and 1.17 for patients with previous angina. Subset case fatality rates were calculated to reflect these odds ratios and preserve the overall estimated case fatality rate for MI. Revascularization Rates We estimated the number of percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) procedures from the NHDS for We adjusted the revascularization rate to reflect a repeated revascularization rate for PCI and for CABG in the first year. We estimated a trend in the ratio of PCI to CABG for 2000 to We assumed that PCI would be included as part of the treatment for MI in the same proportion observed in the NHDS data set for 2000, with emergency CABG complicating 2% of these procedures. We included reductions in mortality and reinfarction rates for patients treated with PCI. 21,22 Risk Functions for Incident Coronary Heart Disease and Noncoronary Heart Disease Death We determined incident coronary heart disease cases (MI, cardiac arrest, or angina) and coronary heart disease and noncoronary heart disease deaths in each risk factor cell for the at-risk US population by using risk functions (r) that incorporated age- and sex-specific parameters ( ) and risk factor-specific -coefficients ( k ;k 1, 2, 3,..., 6), which are constant over the time span of a simulation, and cellspecific risk factor means (m k ;k 1, 2, 3,..., 6), which are altered by user-defined intervention. We determined -coefficients for noncoronary heart disease death from the same examination sets of the Framingham cohort and offspring data that we used for the coronary heart disease -coefficients, but with diastolic blood pressure, smoking, and diabetes as the only statistically significant covariates in the logistic regression analysis. We estimated overall incidence of coronary heart disease and noncoronary heart disease death by age range and sex for 2000 by adjusting the Framingham incidence estimates for 1986 to take into account the trends in risk factor means from 1986 to We estimated the corresponding values of the intercepts by iterative fitting of the risk function to the overall incidence. We estimated incidence of cardiac arrest without previous coronary heart disease by using the proportion of cardiac arrest without previous coronary heart disease in Olmsted County 13 and incidence of MI by using the proportion of MI without previous coronary heart disease in several published studies that analyzed data from the National Registry of Myocardial Infarction 2, 23 the Cardiovascular Cooperative Project, 24 and the Worcester Heart Attack study. 17,25 We assumed all risks and rates to be constant over time, in the absence of evidence of a trend. We incorporated trends as they became apparent, such as that for the use of revascularization between 2000 and the present, but did not project them into the future. The coronary heart disease and noncoronary heart disease death risk functions are applied to every state in every year of a simulation to accommodate the competing risk for these 2 outcomes naturally over time. Incident Coronary Heart Disease Event Allocation We assumed that risk factors would affect the incidence of MI, cardiac arrest, and angina in proportion to overall incidence, except we assumed smoking had a higher relative risk for infarction and cardiac arrest 26 and a proportionately lower coefficient for angina. We assumed that environmental tobacco exposure carried a relative risk of 1.26 for MI and cardiac arrest, compared with nonexposed nonsmokers, 27 but did not influence angina. Risk Factor Prevalence and Correlations between Risk Factors We estimated the prevalence of each risk factor level and correlations among risk factors (and thus the apportionment of the US population without coronary heart disease into the 3240 risk cells) from National Health and Nutrition Examination Study (NHANES), 2003 to Transitions between Risk Factor Levels Transitions from one risk factor level to another were included to preserve the NHANES proportions of the population with each risk factor level. For example, the proportion of 35- to 44-year-old men with low ( 2.6 mol/l [ 100 mg/dl]) LDL cholesterol is For 45- to 54- year-old men, the proportion is The shift toward higher LDL cholesterol levels is most likely caused by increasing LDL levels as people age. In higher age ranges, this trend reverses, so that by age 75 to 84, the proportion is The change in the upper age ranges is most likely related to a more complex array of factors, including the fact that people with higher risk are more likely to die. Annual transfer rates between risk factor levels were calculated to reduce the low risk population from to over 10 years, without regard to the reason for the change, but taking into account the effect of the model s coronary heart disease incidence and noncoronary heart disease death rates in the base case. Because the Coronary Heart Disease Policy Model categorizes risk factors into strata, the transitions between risk factor levels are applied uniformly across all risk factor

10 833.e3 The American Journal of Medicine, Vol 124, No 9, September 2011 subgroups. For example, if the trend in a particular age group is toward higher systolic blood pressure, the transition moves the same proportion of people in the low systolic blood pressure cell to the higher category, without regard to other risk factors. These transitions preserve the NHANES age range specific marginal distributions over time, but cannot represent differential transitions within the risk factor subcategories. Costs We estimated total health care costs from the perspective of the health care system by using national data. 29 We estimated the coronary heart disease cost component by using California data, 19 deflated by using cost-to-charge ratios 30 and the ratio of the US national average costs to the California average, 31 and then inflated to 2010 dollars by using the Bureau of Labor Statistics Consumer Price Index for Medical Care Costs. 32 We based health-related quality of life weights on observational data 33 and discounted costs and quality adjusted life years (QALYs) at a rate of 3% per year in the base case analysis. Immigration People enter the Coronary Heart Disease Policy Model at age 35 and exit the model at age 85 or death. The model does not account for persons who immigrate into or emigrate out of the US population after 35 years of age. Because, on average, the immigration rate exceeds the emigration rate, we would expect that this would bias the model to underestimate the population size and number of annual coronary heart disease events and mortality. If the immigrant population is healthier compared with the general US population, this could bias estimates of the incident and prevalence rates away from the null. Quality Control and Validation The Coronary Heart Disease Policy Model was calibrated to reproduce national data on risk factor distributions, total coronary heart disease deaths, acute MI, witnessed sudden cardiac death, and revascularization procedures in the base year. Validation of projections into the future is an ongoing effort in which the model s results under a broad range of scenarios are compared with data from studies, clinical trials, and surveys, obtained from public sources or by personal communication. Validation required reasonable agreement in outcomes when the conditions that produced the data were incorporated. For example, simulations of persons in the US population age 45 to 64 years that imposed the before and after LDL cholesterol and HDL cholesterol levels recorded in the West of Scotland Coronary Prevention Study (WOSCOPS) 34 resulted in similar results for the cumulative percentage of the cohort to have died of coronary heart disease or have had a first MI, and for the ratio of events in participants who were and were not treated with statins. For validation of cost and cost-effectiveness aspects of the model, the model was used to duplicate a cost-effectiveness analysis of secondary prevention based on the 4S study. 35 With discounting set to 5%, no quality of life adjustments, and coronary heart disease and drug costs only (excludes noncoronary heart disease health care costs), the model produced a cost-effectiveness ratio of $13,100/life year for statins overall in the 35- to 74-year-old secondary prevention population, as compared with age and sex-specific analyses from 4S that estimated ratios ranging from $4700/QALY (70-year-old men) to $18,800/QALY (35- year-old women). 35 Comparison with 1987 Projections In order to estimate the model s performance in previous studies aimed at predicting the future burden of disease, we compared the projections from a 1987 publication to observed statistics from the year The previous projections underestimated the prevalence of future coronary heart disease but overestimated incidence and mortality 36 ; the majority of the variation can be explained by a change in disease definition, and reductions in risk factors, and advances in treatment. In 1987, Weinstein et al 37 projected that the prevalence of coronary heart disease would be 8,385,000 in 2005, which is 34% lower than the estimated prevalence of 12,649,000, according to the 2005 National Health Interview Survey (NHIS). 37 This underprojection is largely explained by a revision of the survey in 1997, and a redefinition of prevalent coronary heart disease. We calculated the average age- and sex-specific prevalence in years 1992 to 1996 and 1997 to 2001 and found that the definition change was associated with an increase in reported prevalence rate of approximately 33%. This change in definition appears to explain the majority of the discrepancy; if the 1987 projections are adjusted to reflect the definition change, there is only a 12% difference between the 1987 model projections and 2005 NHIS estimates. We propose that the remaining 12% higher projected prevalence likely is related to improvements in coronary heart disease risk factors. In 1987, the Coronary Heart Disease Policy Model projected 888,000 incident coronary heart disease events in 2005, whereas the 2006 Heart and Stroke Statistics Update estimated approximately 700,000 incident coronary heart disease events, based on data from the Atherosclerosis Risk in Communities Study (ARIC). This difference is consistent with published temporal trends of coronary heart disease incidence in the US; about a 9% per decade decline in age-adjusted incidence rate, thought largely to be related to improved primary prevention. 38 If we apply the observed temporal trend of a 9% per decade decline in the ageadjusted incidence rate to the estimates from 1987, we observe only a 6% difference between our estimates and those from the 2006 Heart and Stroke Statistics Update. We hypothesize that this difference could be related to the healthy cohort bias, in which participants in studies are often healthier and have lower rates of disease compared with the general population. 39

11 Odden et al Heart Disease in the Aging US Population 833.e4 In 1987, the Coronary Heart Disease Policy Model projected that coronary heart disease mortality would be 608,000 in 2005, whereas data from the US vital statistics reported only 321,000 deaths caused by coronary heart disease in the same year. This trend is consistent with published reports of a decline in coronary heart disease mortality. 40,41 Ford et al 40 described a 27% decline in the coronary heart disease deaths from 1980 to 2000; if the decline in mortality because of improvements in risk factors and treatments is applied to our 1987 projections, they are only 4% higher compared with the vital statistics report. 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